JING LEI, University of California - Berkeley

Main Content

In this talk I will introduce a new class of methods to estimate nonparametric prediction regions that are based on the idea of conformal prediction in machine learning. The new estimator has two desirable properties that are not seen in existing methods: distribution-free finite sample validity and near optimal convergence rate to the oracle. It can be efficiently implemented with a simple rule for parameter tuning. As an important extension, I will also talk about a novel and generic tuning parameter selection method, illustrated through examples in clustering and high-dimensional regression.